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Advances in Energy Efficiency through Neural-Network-Based Models

Author

Listed:
  • L. G. B. Ruiz

    (Department of Software Engineering, University of Granada, 18014 Granada, Spain)

  • M. C. Pegalajar

    (Department of Computer Science and Artificial Intelligence, University of Granada, 18014 Granada, Spain)

Abstract

Currently, new technologies and approaches are continuously and rapidly being introduced and implemented in energy systems [...]

Suggested Citation

  • L. G. B. Ruiz & M. C. Pegalajar, 2023. "Advances in Energy Efficiency through Neural-Network-Based Models," Energies, MDPI, vol. 16(5), pages 1-3, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2258-:d:1081508
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    References listed on IDEAS

    as
    1. Felipe Leite Coelho da Silva & Kleyton da Costa & Paulo Canas Rodrigues & Rodrigo Salas & Javier Linkolk López-Gonzales, 2022. "Statistical and Artificial Neural Networks Models for Electricity Consumption Forecasting in the Brazilian Industrial Sector," Energies, MDPI, vol. 15(2), pages 1-12, January.
    2. L. Cabezón & L. G. B. Ruiz & D. Criado-Ramón & E. J. Gago & M. C. Pegalajar, 2022. "Photovoltaic Energy Production Forecasting through Machine Learning Methods: A Scottish Solar Farm Case Study," Energies, MDPI, vol. 15(22), pages 1-14, November.
    3. Eva Andrés & Manuel Pegalajar Cuéllar & Gabriel Navarro, 2022. "On the Use of Quantum Reinforcement Learning in Energy-Efficiency Scenarios," Energies, MDPI, vol. 15(16), pages 1-24, August.
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